Development of Usability Enhancement Model for Unstructured Big Data Using SLR

Unstructured text contains valuable information for a range of enterprise applications and informed decision making. Text analytics is used to extract valuable insights from unstructured big data. Among the most significant challenges of text analytics, quality and usability are critical in affecting the outcome of the analytical process. The enhancement in usability is important for the exploitation of unstructured data. Most of the existing literature focuses on the usability of structured data as compared to unstructured data whereas big data usability has been discussed merely in the context of its assessment. The existing approaches do not provide proper guidelines on the usability enhancement of unstructured data. In this study, a rigorous systematic literature review using PRISMA framework has been conducted to develop a model enhancing the usability of unstructured data bridging the research gap. The recent approaches and solutions for text analytics have been investigated thoroughly. The usability issues of unstructured text data and their consequences on data preparation for analytics have been identified. Defining the usability dimensions for unstructured big data, identification of the usability determinants, and developing a relationship between usability dimension and determinants to derive usability rules are the significant contributions of this research and are integrated to formulate the usability enhancement model. The proposed model is the major outcome of the research. It contributes to make unstructured data usable and facilitates the data preparation activities with more valuable data that eventually improve the analytical process.

Abstract licence: CC BY-NC-ND

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